Objective To improve the analysis method of the blood components of Yinchenhao decoction （YCHD） in vivo and explore its anti-hepatocellular carcinoma mechanism. Methods Ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry （UPLC-Q-TOF/MS） was used to collect and analyze blood samples from mice. The mice were given a single dose of YCHD with a concentration of 0.1 g/ml and a dose of 25 ml/kg, and then the samples were collected 2 h post–administration, which was to systematically study the chemical components of YCHD in vivo. Network pharmacological methods were used to screen the components and targets of YCHD, and the targets of hepatocellular carcinoma; The common targets of YCHD and hepatocellular carcinoma were identified for GO enrichment and KEGG enrichment. Molecular docking was performed on the main targets to verify the binding ability between the active ingredients and the core targets. The relative mRNA expression levels of serine/threonine-protein kinase（AKT1） and tumor protein p53（TP53） in liver tissues were analyzed via qPCR, including the following mouse groups: mice with concanavalin A（Con-A）-induced acute liver injury without preventive administration, mice with Con-A-induced acute liver injury that received 14 d preventive oral administration of YCHD, and untreated control mice. Results ①The active ingredients of YCHD in the blood were identified by retrieving the data from the in vitro component analysis. They were chrysophanol, herniarin, aloe-emodin, and monotropein. ②The mechanism of action of the blood components against hepatocellular carcinoma （HCC） was further analyzed using network pharmacological methods, and a total of 30 components of YCHD were screened for 213 targets and 215 HCC targets. ③There were 17 intersection targets between YCHD and hepatocellular carcinoma, including AKT1, TP53, receptor tyrosine-protein kinase erbB-2 （ERBB2）, myelocytomatosis oncogene （MYC）, interleukin-1β （IL-1β）, etc. The GO enrichment results indicated that these components were primarily involved in DNA replication，chromosome segregation，leukocyte mediated immunity，leukocyte cell-cell adhesion. The KEGG enrichment results demonstrated that these components were predominantly associated with diverse cancer pathways. Additionally, the results indicated involvement in the citrate cycle （TCA cycle）, pyruvate metabolism, and p53 signaling pathway, ect. ④The results of molecular docking showed that chrysophanol, herniarin, and aloe - emodin had strong binding abilities with AKT1, TP53, ERBB2, MYC, and IL-1β. ⑤The relative expression of AKT1 and TP53 mRNA was significantly higher in the modelling group than in the control group. The relative expression of AKT1 and TP53 mRNA was significantly lower in the drug administration group than in the modelling group. Conclusion There were 4 blood components in YCHD, among which chrysophanol, herniarin, and aloe-emodin may act on AKT1, TP53, ERBB2, MYC, IL-1β and then participated in the regulation of cancer signaling pathways and p53 signaling pathway to play a role in the treatment of HCC.

Objective:To investigate the serum metabonomics of pancreatic cancer and chronic pancreatitis patients and screen the differential metabolites for differentiating pancreatic cancer from chronic pancreatis.Methods:From June 2021 to June 2022, the clinical data of 54 patients diagnosed with pancreatic ductal adenocarcinoma in the Department of Hepatobiliary, Pancreatic and Splenic Surgery of the First Affiliated Hospital of Naval Medical University were collected. A total of 54 patients with chronic pancreatitis who were admitted at the same time were selected as the control group. UHPLC/Q-TOF MS-based metabonomics techniques were applied to analyze the difference in serum metabolites in the two groups with multivariate and univariate statistical method, and the different metabolites were screened and identified in accordance with the molecular weight, metabolites databases and mass spectrometry (MS)/MS information. Two-thirds of the cases were randomly selected from patients with pancreatic cancer and chronic pancreatitis as the modeling population, and the remaining population was used as the validation population. In the modeled population, the receiver operating characteristic curve (ROC) of the screened differential metabolites was plotted and the area under the curve (AUC) was calculated. Differential metabolite variables with AUC ≥75%, a VIP value ≥1.5 were selected into the logistic multivariate regression analysis model, and the regression equation and the regression coefficients of each selected independent variable were obtained by stepwise regression by backward method. The final selected differential metabolites were evaluated by the formula P=1/{1+Exp[-(β0+β1X1+β1X1+…+βiXi)]} to establish a diagnostic model and predict the clinical application value of the model by evaluating it compared to the CA19-9 ROC curve. At the same time, the non-parametric Bootstrap method was used to verify the diagnostic performance of the model in the validation population. Results:There were 18 kinds of different serum metabolits in the final screening and identification in the two groups. The level of hypoxanthine, L-carnitine, acetylcarnitine, C16 sphinganine, linoleic acid, palmitoylcarnitine, linoleyl carnitine, uracil deoxynucleotide, glycocholic chenodeoxycholic acid in serum of pancreatic cancer patients were higher than that in the chronic pancreatitis patients; Uric acid, tryptophan, indoxylsulfuric acid, 1-palmitoyl lysophosphatidic acid, LPA(18∶2/0∶0), LysoPE (18∶1/0∶0), LysoPC (14∶0), LysoPC(15∶0), LysoPC(16∶1) in the serum were lower in patients with pancreatic cancer compared with that in chronic pancreatitis patients, and all the differences were statistically significant (all P value <0.05). In the modeled population, the ROC curve was established according to the peak intensity of 18 differential metabolites, and the metabolic differentiators with AUC of ≥75% and VIP value of ≥1.5 were selected for logistic multivariate analysis, and finally linoleoleic carnitine and LPA (18∶2/0∶0) were included in the logistic regression model. The prediction model of pancreatic cancer with two serum metabolites linoleyl carnitine and LPA (18∶2/0∶0) was established. The AUC value (95% CI) of the prediction model was 0.91 (0.85-0.97), which was higher than that of CA19-9 (0.85, 0.76-0.94), the sensitivity and specificity were 86.4% and 80.6%, respectively, and the sensitivity was higher than that of CA19-9 (77.3%), but the specificity was lower than that of CA19-9 (91.7%). Internal validation showed than the AUC value (95% CI) of the prediction model was 0.91 (0.79-0.94), which was higher than that of CA19-9 ( P<0.05). Conclusions:The serum metabolites linolein carnitine and LPA(18∶2/0∶0) may be potential diagnostic markers to distinguish pancreatic cancer from patients with chronic pancreatitis.

Objective:To investigate the serum metabonomics of pancreatic cancer and chronic pancreatitis patients and screen the differential metabolites for differentiating pancreatic cancer from chronic pancreatis.Methods:From June 2021 to June 2022, the clinical data of 54 patients diagnosed with pancreatic ductal adenocarcinoma in the Department of Hepatobiliary, Pancreatic and Splenic Surgery of the First Affiliated Hospital of Naval Medical University were collected. A total of 54 patients with chronic pancreatitis who were admitted at the same time were selected as the control group. UHPLC/Q-TOF MS-based metabonomics techniques were applied to analyze the difference in serum metabolites in the two groups with multivariate and univariate statistical method, and the different metabolites were screened and identified in accordance with the molecular weight, metabolites databases and mass spectrometry (MS)/MS information. Two-thirds of the cases were randomly selected from patients with pancreatic cancer and chronic pancreatitis as the modeling population, and the remaining population was used as the validation population. In the modeled population, the receiver operating characteristic curve (ROC) of the screened differential metabolites was plotted and the area under the curve (AUC) was calculated. Differential metabolite variables with AUC ≥75%, a VIP value ≥1.5 were selected into the logistic multivariate regression analysis model, and the regression equation and the regression coefficients of each selected independent variable were obtained by stepwise regression by backward method. The final selected differential metabolites were evaluated by the formula P=1/{1+Exp[-(β0+β1X1+β1X1+…+βiXi)]} to establish a diagnostic model and predict the clinical application value of the model by evaluating it compared to the CA19-9 ROC curve. At the same time, the non-parametric Bootstrap method was used to verify the diagnostic performance of the model in the validation population. Results:There were 18 kinds of different serum metabolits in the final screening and identification in the two groups. The level of hypoxanthine, L-carnitine, acetylcarnitine, C16 sphinganine, linoleic acid, palmitoylcarnitine, linoleyl carnitine, uracil deoxynucleotide, glycocholic chenodeoxycholic acid in serum of pancreatic cancer patients were higher than that in the chronic pancreatitis patients; Uric acid, tryptophan, indoxylsulfuric acid, 1-palmitoyl lysophosphatidic acid, LPA(18∶2/0∶0), LysoPE (18∶1/0∶0), LysoPC (14∶0), LysoPC(15∶0), LysoPC(16∶1) in the serum were lower in patients with pancreatic cancer compared with that in chronic pancreatitis patients, and all the differences were statistically significant (all P value <0.05). In the modeled population, the ROC curve was established according to the peak intensity of 18 differential metabolites, and the metabolic differentiators with AUC of ≥75% and VIP value of ≥1.5 were selected for logistic multivariate analysis, and finally linoleoleic carnitine and LPA (18∶2/0∶0) were included in the logistic regression model. The prediction model of pancreatic cancer with two serum metabolites linoleyl carnitine and LPA (18∶2/0∶0) was established. The AUC value (95% CI) of the prediction model was 0.91 (0.85-0.97), which was higher than that of CA19-9 (0.85, 0.76-0.94), the sensitivity and specificity were 86.4% and 80.6%, respectively, and the sensitivity was higher than that of CA19-9 (77.3%), but the specificity was lower than that of CA19-9 (91.7%). Internal validation showed than the AUC value (95% CI) of the prediction model was 0.91 (0.79-0.94), which was higher than that of CA19-9 ( P<0.05). Conclusions:The serum metabolites linolein carnitine and LPA(18∶2/0∶0) may be potential diagnostic markers to distinguish pancreatic cancer from patients with chronic pancreatitis.

With the rapid developments of science and technology, the diagnosis technology based on nuclear physics and radiotherapy technology are widely used in medicine, but radiation at the same time could have different levels of damage to human body. Therefore, it is of great significance to research and develop drugs that can prevent and treat radiation damage. The research progresses and prospects of radiation-resistant natural products, such as polysaccharides, flavonoids, phenolic acids, saponins and so on, were reviewed in this paper in order to provide a reference for further developments.